Overview

Dataset statistics

Number of variables39
Number of observations10324
Missing cells3706
Missing cells (%)0.9%
Duplicate rows31
Duplicate rows (%)0.3%
Total size in memory3.1 MiB
Average record size in memory312.0 B

Variable types

Numeric8
Categorical31

Alerts

Dataset has 31 (0.3%) duplicate rowsDuplicates
Line Item Quantity is highly correlated with Line Item Value and 3 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Pack Price is highly correlated with Unit Price and 1 other fieldsHigh correlation
Unit Price is highly correlated with Pack Price and 3 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Unit Price and 4 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit Price and 4 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Pack Price and 5 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Insurance (USD)High correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 2 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Pack Price is highly correlated with Unit PriceHigh correlation
Unit Price is highly correlated with Pack PriceHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Weight (Kilograms)High correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 2 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Sub Classification_Adult and 3 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
Sub Classification_Adult is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTMHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_HRDT is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Product Group_ARV is highly correlated with Sub Classification_HIV test and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_From RDC and 5 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Unit of Measure (Per Pack) is highly correlated with Product Group_ACT and 4 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_Ethiopia Field Office is highly correlated with Managed By_Haiti Field OfficeHigh correlation
Managed By_Haiti Field Office is highly correlated with Managed By_Ethiopia Field OfficeHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 6 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 8 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Vendor INCO Term_DDP and 2 other fieldsHigh correlation
Shipment Mode_Truck is highly correlated with Vendor INCO Term_EXW and 1 other fieldsHigh correlation
Product Group_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ARV is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Sub Classification_Adult is highly correlated with Vendor INCO Term_EXW and 4 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Unit of Measure (Per Pack) and 2 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Weight (Kilograms) has 1633 (15.8%) missing values Missing
Freight Cost (USD) has 1786 (17.3%) missing values Missing
Line Item Insurance (USD) has 287 (2.8%) missing values Missing
Unit Price is highly skewed (γ1 = 40.58484939) Skewed
Weight (Kilograms) is highly skewed (γ1 = 33.03598689) Skewed

Reproduction

Analysis started2022-05-21 12:07:59.098396
Analysis finished2022-05-21 12:08:22.676172
Duration23.58 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unit of Measure (Per Pack)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.990895
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:22.774952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q130
median60
Q390
95-th percentile240
Maximum1000
Range999
Interquartile range (IQR)60

Descriptive statistics

Standard deviation76.57976396
Coefficient of variation (CV)0.9819064643
Kurtosis36.09399876
Mean77.990895
Median Absolute Deviation (MAD)30
Skewness4.302502487
Sum805178
Variance5864.460248
MonotonicityNot monotonic
2022-05-21T17:38:22.895675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
604121
39.9%
302630
25.5%
100976
 
9.5%
240670
 
6.5%
120474
 
4.6%
20470
 
4.6%
90222
 
2.2%
300157
 
1.5%
1126
 
1.2%
25114
 
1.1%
Other values (21)364
 
3.5%
ValueCountFrequency (%)
1126
 
1.2%
24
 
< 0.1%
38
 
0.1%
54
 
< 0.1%
122
 
< 0.1%
184
 
< 0.1%
20470
 
4.6%
242
 
< 0.1%
25114
 
1.1%
302630
25.5%
ValueCountFrequency (%)
100016
 
0.2%
7205
 
< 0.1%
5407
 
0.1%
33639
 
0.4%
300157
 
1.5%
27053
 
0.5%
240670
6.5%
20076
 
0.7%
18076
 
0.7%
1683
 
< 0.1%

Line Item Quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18332.53487
Minimum1
Maximum619999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:23.035301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1408
median3000
Q317039.75
95-th percentile90951.55
Maximum619999
Range619998
Interquartile range (IQR)16631.75

Descriptive statistics

Standard deviation40035.30296
Coefficient of variation (CV)2.18383891
Kurtosis40.0503001
Mean18332.53487
Median Absolute Deviation (MAD)2950
Skewness5.038314699
Sum189265090
Variance1602825483
MonotonicityNot monotonic
2022-05-21T17:38:23.179914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000093
 
0.9%
100091
 
0.9%
10087
 
0.8%
200073
 
0.7%
500069
 
0.7%
50067
 
0.6%
2000067
 
0.6%
300066
 
0.6%
363
 
0.6%
5000062
 
0.6%
Other values (5055)9586
92.9%
ValueCountFrequency (%)
135
0.3%
240
0.4%
363
0.6%
446
0.4%
528
0.3%
648
0.5%
727
0.3%
826
0.3%
922
 
0.2%
1054
0.5%
ValueCountFrequency (%)
6199991
 
< 0.1%
6009061
 
< 0.1%
5551971
 
< 0.1%
5150003
< 0.1%
5145261
 
< 0.1%
4600411
 
< 0.1%
4400001
 
< 0.1%
4384091
 
< 0.1%
4019611
 
< 0.1%
4000002
< 0.1%

Line Item Value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157650.5673
Minimum0
Maximum5951990.4
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:23.367413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192.5755
Q14314.5925
median30471.465
Q3166447.14
95-th percentile702831
Maximum5951990.4
Range5951990.4
Interquartile range (IQR)162132.5475

Descriptive statistics

Standard deviation345292.067
Coefficient of variation (CV)2.190236755
Kurtosis54.15243042
Mean157650.5673
Median Absolute Deviation (MAD)29920.465
Skewness5.837020186
Sum1627584457
Variance1.192266115 × 1011
MonotonicityNot monotonic
2022-05-21T17:38:23.515059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000029
 
0.3%
1600023
 
0.2%
80018
 
0.2%
017
 
0.2%
1440016
 
0.2%
24421615
 
0.1%
320015
 
0.1%
12000013
 
0.1%
25011
 
0.1%
16011
 
0.1%
Other values (8731)10156
98.4%
ValueCountFrequency (%)
017
0.2%
0.011
 
< 0.1%
0.031
 
< 0.1%
0.121
 
< 0.1%
0.21
 
< 0.1%
0.241
 
< 0.1%
0.251
 
< 0.1%
0.421
 
< 0.1%
0.51
 
< 0.1%
0.71
 
< 0.1%
ValueCountFrequency (%)
5951990.41
< 0.1%
5768697.61
< 0.1%
5329891.21
< 0.1%
5140114.741
< 0.1%
4959241.981
< 0.1%
4278871.841
< 0.1%
4228629.721
< 0.1%
40140001
< 0.1%
39328801
< 0.1%
39040002
< 0.1%

Pack Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1175
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.91024119
Minimum0
Maximum1345.64
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:23.662655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q14.12
median9.3
Q323.5925
95-th percentile80
Maximum1345.64
Range1345.64
Interquartile range (IQR)19.4725

Descriptive statistics

Standard deviation45.60922308
Coefficient of variation (CV)2.081639481
Kurtosis293.1762044
Mean21.91024119
Median Absolute Deviation (MAD)6.57
Skewness12.98843214
Sum226201.33
Variance2080.20123
MonotonicityNot monotonic
2022-05-21T17:38:23.809232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32368
 
3.6%
80307
 
3.0%
89183
 
1.8%
11.22139
 
1.3%
20110
 
1.1%
8.7691
 
0.9%
1.9591
 
0.9%
2.4490
 
0.9%
2.189
 
0.9%
2.2688
 
0.9%
Other values (1165)8768
84.9%
ValueCountFrequency (%)
018
 
0.2%
0.0185
0.8%
0.392
 
< 0.1%
0.72
 
< 0.1%
0.94
 
< 0.1%
1.12
 
< 0.1%
1.142
 
< 0.1%
1.171
 
< 0.1%
1.21
 
< 0.1%
1.211
 
< 0.1%
ValueCountFrequency (%)
1345.641
 
< 0.1%
12501
 
< 0.1%
1242.533
 
< 0.1%
750.291
 
< 0.1%
7001
 
< 0.1%
4009
 
0.1%
35039
0.4%
308.173
 
< 0.1%
306.883
 
< 0.1%
301.531
 
< 0.1%

Unit Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct183
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6117008911
Minimum0
Maximum238.65
Zeros103
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:23.972793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.16
Q30.47
95-th percentile1.6
Maximum238.65
Range238.65
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation3.275807741
Coefficient of variation (CV)5.35524435
Kurtosis2725.960252
Mean0.6117008911
Median Absolute Deviation (MAD)0.12
Skewness40.58484939
Sum6315.2
Variance10.73091635
MonotonicityNot monotonic
2022-05-21T17:38:24.235129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04713
 
6.9%
0.01492
 
4.8%
0.12464
 
4.5%
0.14444
 
4.3%
0.8411
 
4.0%
0.11400
 
3.9%
1.6368
 
3.6%
0.05343
 
3.3%
0.16343
 
3.3%
0.19321
 
3.1%
Other values (173)6025
58.4%
ValueCountFrequency (%)
0103
 
1.0%
0.01492
4.8%
0.02140
 
1.4%
0.03250
 
2.4%
0.04713
6.9%
0.05343
3.3%
0.06274
 
2.7%
0.07248
 
2.4%
0.08146
 
1.4%
0.09154
 
1.5%
ValueCountFrequency (%)
238.651
 
< 0.1%
41.681
 
< 0.1%
37.52
 
< 0.1%
301
 
< 0.1%
26.911
 
< 0.1%
254
 
< 0.1%
24.853
 
< 0.1%
24.546
0.4%
2323
0.2%
17.123
 
< 0.1%

Weight (Kilograms)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct3388
Distinct (%)39.0%
Missing1633
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean4464.293407
Minimum0
Maximum857354
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:24.399683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1275
median1303
Q34364
95-th percentile18141
Maximum857354
Range857354
Interquartile range (IQR)4089

Descriptive statistics

Standard deviation13372.40941
Coefficient of variation (CV)2.995414545
Kurtosis1942.060991
Mean4464.293407
Median Absolute Deviation (MAD)1213
Skewness33.03598689
Sum38799174
Variance178821333.3
MonotonicityNot monotonic
2022-05-21T17:38:24.567245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11231
 
0.3%
5430
 
0.3%
229
 
0.3%
628
 
0.3%
123
 
0.2%
4623
 
0.2%
1221
 
0.2%
1320
 
0.2%
520
 
0.2%
6020
 
0.2%
Other values (3378)8446
81.8%
(Missing)1633
 
15.8%
ValueCountFrequency (%)
01
 
< 0.1%
123
0.2%
229
0.3%
319
0.2%
419
0.2%
520
0.2%
628
0.3%
716
0.2%
814
0.1%
917
0.2%
ValueCountFrequency (%)
8573541
 
< 0.1%
2910961
 
< 0.1%
2055031
 
< 0.1%
1547801
 
< 0.1%
1120273
< 0.1%
904462
 
< 0.1%
887615
< 0.1%
881901
 
< 0.1%
870762
 
< 0.1%
851281
 
< 0.1%

Freight Cost (USD)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5432
Distinct (%)63.6%
Missing1786
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean12641.90846
Minimum0.75
Maximum289653.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:24.717799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile732.2225
Q12599.09
median6759.28
Q315571.48
95-th percentile43548
Maximum289653.2
Range289652.45
Interquartile range (IQR)12972.39

Descriptive statistics

Standard deviation18189.92402
Coefficient of variation (CV)1.43885902
Kurtosis27.76382653
Mean12641.90846
Median Absolute Deviation (MAD)5067.96
Skewness4.189276228
Sum107936614.4
Variance330873335.8
MonotonicityNot monotonic
2022-05-21T17:38:24.852476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9736.137
 
0.4%
7445.828
 
0.3%
6147.1827
 
0.3%
7329.8320
 
0.2%
9341.4919
 
0.2%
13398.0618
 
0.2%
9869.5517
 
0.2%
1709016
 
0.2%
20499.3315
 
0.1%
3918.3715
 
0.1%
Other values (5422)8326
80.6%
(Missing)1786
 
17.3%
ValueCountFrequency (%)
0.751
 
< 0.1%
14.362
< 0.1%
17.721
 
< 0.1%
22.291
 
< 0.1%
29.213
< 0.1%
301
 
< 0.1%
30.491
 
< 0.1%
411
 
< 0.1%
42.351
 
< 0.1%
481
 
< 0.1%
ValueCountFrequency (%)
289653.21
 
< 0.1%
241407.271
 
< 0.1%
194623.442
 
< 0.1%
161962.321
 
< 0.1%
161712.8713
0.1%
152368.71
 
< 0.1%
146850.661
 
< 0.1%
146734.851
 
< 0.1%
139951.341
 
< 0.1%
132890.274
 
< 0.1%

Line Item Insurance (USD)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6722
Distinct (%)67.0%
Missing287
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean240.1176258
Minimum0
Maximum7708.44
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2022-05-21T17:38:24.997051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q16.51
median47.04
Q3252.4
95-th percentile1082.032
Maximum7708.44
Range7708.44
Interquartile range (IQR)245.89

Descriptive statistics

Standard deviation500.1905677
Coefficient of variation (CV)2.083106419
Kurtosis34.91121486
Mean240.1176258
Median Absolute Deviation (MAD)46.27
Skewness4.827162374
Sum2410060.61
Variance250190.604
MonotonicityNot monotonic
2022-05-21T17:38:25.135680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
054
 
0.5%
0.0237
 
0.4%
0.0733
 
0.3%
0.0530
 
0.3%
0.0630
 
0.3%
0.0126
 
0.3%
0.0323
 
0.2%
0.0921
 
0.2%
0.0820
 
0.2%
0.1218
 
0.2%
Other values (6712)9745
94.4%
(Missing)287
 
2.8%
ValueCountFrequency (%)
054
0.5%
0.0126
0.3%
0.0237
0.4%
0.0323
0.2%
0.0414
 
0.1%
0.0530
0.3%
0.0630
0.3%
0.0733
0.3%
0.0820
 
0.2%
0.0921
 
0.2%
ValueCountFrequency (%)
7708.441
< 0.1%
7005.491
< 0.1%
5930.221
< 0.1%
5573.311
< 0.1%
5479.131
< 0.1%
5284.041
< 0.1%
5230.811
< 0.1%
5162.291
< 0.1%
51452
< 0.1%
5098.11
< 0.1%

Managed By_Ethiopia Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10323 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010323
> 99.9%
11
 
< 0.1%

Length

2022-05-21T17:38:25.264336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.328206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010323
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_Haiti Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10323 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010323
> 99.9%
11
 
< 0.1%

Length

2022-05-21T17:38:25.391039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.454868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010323
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_PMO - US
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
10265 
0
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110265
99.4%
059
 
0.6%

Length

2022-05-21T17:38:25.516704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.579531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
110265
99.4%
059
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_South Africa Field Office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10267 
1
 
57

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010267
99.4%
157
 
0.6%

Length

2022-05-21T17:38:25.646355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.710185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010267
99.4%
157
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_Direct Drop
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
5404 
1
4920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
05404
52.3%
14920
47.7%

Length

2022-05-21T17:38:25.775011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.838840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
05404
52.3%
14920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
5404 
0
4920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
15404
52.3%
04920
47.7%

Length

2022-05-21T17:38:25.901633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:25.965502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
15404
52.3%
04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10321 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010321
> 99.9%
13
 
< 0.1%

Length

2022-05-21T17:38:26.028292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.092123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010321
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10049 
1
 
275

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010049
97.3%
1275
 
2.7%

Length

2022-05-21T17:38:26.155991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.342453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010049
97.3%
1275
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10315 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010315
99.9%
19
 
0.1%

Length

2022-05-21T17:38:26.406321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.473102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010315
99.9%
19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DDP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
8881 
1
1443 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08881
86.0%
11443
 
14.0%

Length

2022-05-21T17:38:26.536933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.602757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
08881
86.0%
11443
 
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10309 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010309
99.9%
115
 
0.1%

Length

2022-05-21T17:38:26.666587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.731411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010309
99.9%
115
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_EXW
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
7546 
1
2778 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
07546
73.1%
12778
 
26.9%

Length

2022-05-21T17:38:26.794245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.860066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
07546
73.1%
12778
 
26.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
9927 
1
 
397

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09927
96.2%
1397
 
3.8%

Length

2022-05-21T17:38:26.924933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:26.991714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09927
96.2%
1397
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_N/A - From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
5404 
0
4920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
15404
52.3%
04920
47.7%

Length

2022-05-21T17:38:27.056582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.123364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
15404
52.3%
04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Air
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
6113 
0
4211 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16113
59.2%
04211
40.8%

Length

2022-05-21T17:38:27.189186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.258043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
16113
59.2%
04211
40.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
9674 
1
 
650

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09674
93.7%
1650
 
6.3%

Length

2022-05-21T17:38:27.322870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.388693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09674
93.7%
1650
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
9953 
1
 
371

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09953
96.4%
1371
 
3.6%

Length

2022-05-21T17:38:27.453480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.518307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09953
96.4%
1371
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Truck
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
7494 
1
2830 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07494
72.6%
12830
 
27.4%

Length

2022-05-21T17:38:27.583133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.648957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
07494
72.6%
12830
 
27.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10308 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010308
99.8%
116
 
0.2%

Length

2022-05-21T17:38:27.732772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.796600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010308
99.8%
116
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ANTM
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10302 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010302
99.8%
122
 
0.2%

Length

2022-05-21T17:38:27.921268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:27.987097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010302
99.8%
122
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ARV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
8550 
0
1774 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18550
82.8%
01774
 
17.2%

Length

2022-05-21T17:38:28.049921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.113750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18550
82.8%
01774
 
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_HRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
8596 
1
1728 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08596
83.3%
11728
 
16.7%

Length

2022-05-21T17:38:28.176581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.240411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
08596
83.3%
11728
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_MRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10316 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010316
99.9%
18
 
0.1%

Length

2022-05-21T17:38:28.423884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.494693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010316
99.9%
18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10308 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010308
99.8%
116
 
0.2%

Length

2022-05-21T17:38:28.561555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.625413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010308
99.8%
116
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Adult
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
6595 
0
3729 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16595
63.9%
03729
36.1%

Length

2022-05-21T17:38:28.688212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.751044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
16595
63.9%
03729
36.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
8757 
1
1567 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08757
84.8%
11567
 
15.2%

Length

2022-05-21T17:38:28.813880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:28.878706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
08757
84.8%
11567
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10163 
1
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010163
98.4%
1161
 
1.6%

Length

2022-05-21T17:38:28.943532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:29.007359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010163
98.4%
1161
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Malaria
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
10294 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010294
99.7%
130
 
0.3%

Length

2022-05-21T17:38:29.069196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:29.134002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
010294
99.7%
130
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Pediatric
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
8369 
1
1955 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08369
81.1%
11955
 
18.9%

Length

2022-05-21T17:38:29.206789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:29.277638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
08369
81.1%
11955
 
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_No
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0
7030 
1
3294 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07030
68.1%
13294
31.9%

Length

2022-05-21T17:38:29.348449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:29.418262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
07030
68.1%
13294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_Yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1
7030 
0
3294 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17030
68.1%
03294
31.9%

Length

2022-05-21T17:38:29.488075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:38:29.556851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
17030
68.1%
03294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-21T17:38:18.921730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:11.405356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.388725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.491738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.466182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.458525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.642753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.677932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.060342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:11.538007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.514354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.610461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.588852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.574216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.785329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.809564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.203492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:11.660678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.655017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.731139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.714557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.698883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.916007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.969137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.341162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:11.781355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.784632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.850815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.839183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.821554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.042641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:18.188555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.478796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:11.903029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.910334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.973450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.966883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.968206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.164317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:18.364082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.610443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.016688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.028019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.091184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.085524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.119756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.292973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:18.509733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.759038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.135404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.237460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.206875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.201254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.260380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.409637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:18.647429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:19.897636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:12.258079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:13.362124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:14.333537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:15.323926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:16.505121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:17.533348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:38:18.779096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-21T17:38:29.672609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T17:38:30.199137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T17:38:30.723768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T17:38:31.322128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T17:38:31.783891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T17:38:20.321539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T17:38:21.768633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-21T17:38:22.126643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-21T17:38:22.285259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
03019551.0029.000.9713.0780.34NaN0010100000010010000001000100001
124010006200.006.200.03358.04521.50NaN0010100000010010000010000000101
210050040000.0080.000.80171.01653.78NaN0010100000001010000001000100001
36031920127360.803.990.071855.016007.06NaN0010100000010010000010001000001
46038000121600.003.200.057590.045450.08NaN0010100000010010000010001000001
52404162225.605.350.02504.05920.42NaN0010100000010010000010000000101
6901354374.0032.400.36328.0NaNNaN0010100000100010000010000000101
7601666760834.553.650.061478.06212.41NaN0010100000010010000010001000001
860273532.351.950.03479.04861.14NaN0010100000010010000010001000010
91202800115080.0041.100.34643.0NaNNaN0010100100000010000010001000001

Last rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
10314601034037224.003.600.066295.016941.0038.270010010000000101000010000000110
10315120700001304800.0018.640.1615198.026180.001341.330010010000000101000010001000001
10316601500097800.006.520.111547.03410.00115.110010010000000101000010001000001
1031730672420978.883.120.106183.047281.5624.690010010000000110000010001000010
1031860205243738874.803.600.0625880.046111.55869.660010010000000100010010000000110
1031960166571599655.603.600.0625880.046111.55705.790010010000000100010010000000110
103206021072137389.446.520.114426.014734.92161.710010010000000100010010001000010
10321305145265140114.749.990.33NaNNaN5284.040010010000000100010010001000010
103226017465113871.806.520.111392.0NaN134.030010010000000100010010001000001
10323603663972911.611.990.03NaNNaN85.820010010000000100010010000000110

Duplicate rows

Most frequently occurring

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes# duplicates
231002744244216.089.00.891186.024927.19342.3900101000000100100000010001000016
01298873206.024.524.501992.026266.3490.5600101000000100100000010001000015
11351080730.023.023.002535.020083.74158.2300101000000100100000010001000013
251002744244216.089.00.891303.028734.43342.3900101000000100100000010001000013
21351080730.023.023.002535.023035.69158.2300101000000100100000010001000012
315994146853.024.524.503996.053062.21150.9600101000000100100000010001000012
416998160954.023.023.005057.044347.71257.5300101000000100100000010001000012
517002161046.023.023.005057.044347.71257.6700101000000100100000010001000012
6310250.025.08.336.0418.940.4900101000000100100000010000100012
72025800.032.01.60176.04124.740.9900101000000100100000010001000102